An open-pit coal mine coal production and sales intelligent optimization method and device

Abstract

This invention discloses an intelligent optimization method and equipment for coal production and sales in open-pit coal mines. Based on original geological information, the mined coal seam is divided into multiple zones, and a coal seam data model is established. A coal washing plant data model is established based on the amount of over-screened coal, under-screened coal, gangue rate, and coal quality testing information of each coal type from the coal washing plant's screening workshop. The correspondence between the coal seam data model and the coal washing plant data model is determined. The amount of under-screened coal is determined based on long-term agreements with the power plant and the unit's operating status. Based on the correspondence, the screening ratio of the over-screened coal and the size of the clean coal are adjusted to formulate an optimal sales plan. Through the above technical solution, the fixed coal blending amount for the power plant can be determined according to the long-term agreements with the power plant and the high and low load operating status. Based on the geological information of the coal seam and the relationship between different coal types in the coal washing plant, combined with the customer's required coal calorific value and clean coal size, the optimal sales plan is determined.

Description

Technical Field

[0001] This application relates to the field of intelligent coal production, and more specifically, to a method and equipment for intelligent optimization of coal production and sales in open-pit coal mines. Background Technology

[0002] The current coal-power integration project's production organization process is basically based on the needs of the coal flow system, following the operational flow of coal mine → coal washing plant → power plant. Daily coal mining volume at the coal mine is organized according to the actual coal storage conditions in the raw coal silos and the coal storage volume in the finished product silos of the coal washing plant. The sales volume of refined coal from the coal washing plant is adjusted based on market demand and the amount of refined coal in the finished product silos, using experience-based estimations and screening adjustments. Power plant coal consumption is coordinated with the coal mine to organize coal transportation, based on the specific conditions of the coal mine silos and the power plant's coal storage silos, while ensuring the unit load requirements are met.

[0003] Because coal-power integration projects rely solely on a simple supply-demand relationship for coal allocation, the production organization, refined coal sales, and power plant coal consumption are often disparate. Coal quality information at the mine production side is only available on an ad-hoc basis, without advance access to seam quality data. This makes it impossible to determine the optimal blending ratio of high-quality and low-calorific-value coal, and production organization focuses solely on supply based on quantity. At the coal washing plant, washing is based on the amount of coal delivered at the mine, and the washing media and gangue content cannot be predetermined. This leads to significant fluctuations in refined coal production, and the calorific value of refined coal cannot be predicted in advance based on customer demand, hindering the scientific allocation of sales vehicles. Power plant coal consumption also lacks a fixed supply model based on long-term contracts and varying load conditions. Therefore, a scientific and intelligent optimization method for coal production, sales, and power generation is missing.

[0004] Therefore, how to provide an intelligent optimization method for coal production and sales in open-pit coal mines to achieve mutual linkage between coal production, sales and power plants, and to realize intelligent management of the entire coal production process, has become an urgent technical problem to be solved in this field. Summary of the Invention

[0005] This invention provides an intelligent optimization method for coal production and sales in open-pit coal mines, addressing the problem in existing coal-power integrated projects where coal allocation is based solely on a supply-demand relationship among production organization, refined coal sales, and power plant coal consumption. The method includes:

[0006] Based on the original geological information, the coal seam to be mined is divided into multiple zones, and a coal seam data model is established.

[0007] A coal washing plant data model is established based on the amount of coal on the screen, the amount of coal under the screen, the gangue rate, and the coal quality test information of each type of coal in the coal washing plant screening workshop, and the correspondence between the coal seam data model and the coal washing plant data model is determined.

[0008] The amount of coal under the screen is determined based on the power plant's long-term agreement and the unit's operating status. Based on the corresponding relationship, the screening ratio of the coal over the screen and the size of the clean coal lumps are adjusted to formulate the optimal sales plan.

[0009] In some embodiments of this application, the coal seam to be mined is divided into multiple zones based on the original geological information, and a coal seam data model is established, specifically as follows:

[0010] Based on the original geological information, additional exploration boreholes were drilled in the mined coal seam, and the mined coal seam was divided into multiple zones;

[0011] Determine the coal pillar structure within each zone, wherein the coal pillar structure includes the calorific value of the coal, the hardness of the coal, and the gangue content;

[0012] Based on the coal pillar structure, the coal seam structure and coal quality information of each zone are determined, and a coal seam data model is established.

[0013] In some embodiments of this application, the unit operating state includes full load state and low load state.

[0014] In some embodiments of this application, the amount of under-screened coal is determined based on the power plant's long-term agreement and the unit's operating status, and the screening ratio and clean coal lump size are adjusted based on the corresponding relationship, specifically as follows:

[0015] Determine the amount of coal passing through the first screen when the power plant is at full load and the amount of coal passing through the second screen when the power plant is at low load.

[0016] The amount of coal passing through the first screen and the amount of coal passing through the second sun are determined based on the amount of coal passing through the first screen and the amount of coal passing through the second screen respectively. The amount of coal passing through the first screen is the amount of coal remaining in the coal washing plant when the power plant is at full load, and the amount of coal passing through the second screen is the amount of coal remaining in the coal washing plant when the power plant is at low load.

[0017] Based on the aforementioned correspondence, the screening ratio and fine coal lump size of the coal quantity on the first or second screen are adjusted, and the optimal sales plan is determined.

[0018] In some embodiments of this application, the method further includes:

[0019] Based on the operating status and the corresponding relationship, the mining plan for each zone of the coal seam is determined, and coal mining is carried out according to the mining plan;

[0020] Based on the optimal sales plan, the blasting hole parameters or charge structure of the mining plan are adjusted to adjust the size of the coal blocks.

[0021] Accordingly, the present invention also proposes an intelligent optimization device for coal production and sales in open-pit coal mines, the device comprising:

[0022] A module is established to divide the coal seam into multiple zones based on the original geological information and to build a coal seam data model.

[0023] The determination module is used to establish a coal washing plant data model based on the amount of coal on the screen, the amount of coal under the screen, the gangue rate, and the coal quality test information of each type of coal in the coal washing plant screening workshop, and to determine the correspondence between the coal seam data model and the coal washing plant data model.

[0024] The module is used to determine the amount of under-screened coal based on the power plant's long-term agreement and the unit's operating status, and to adjust the screening ratio and fine coal lump size of the over-screened coal based on the corresponding relationship, thereby formulating the optimal sales plan.

[0025] In some embodiments of this application, the establishment module is specifically used for:

[0026] Based on the original geological information, additional exploration boreholes were drilled in the mined coal seam, and the mined coal seam was divided into multiple zones;

[0027] Determine the coal pillar structure within each zone, wherein the coal pillar structure includes the calorific value of the coal, the hardness of the coal, and the gangue content;

[0028] Based on the coal pillar structure, the coal seam structure and coal quality information of each zone are determined, and a coal seam data model is established.

[0029] In some embodiments of this application, the unit operating state includes full load state and low load state.

[0030] In some embodiments of this application, the formulation module is specifically used for:

[0031] Determine the amount of coal passing through the first screen when the power plant is at full load and the amount of coal passing through the second screen when the power plant is at low load.

[0032] The amount of coal passing through the first screen and the amount of coal passing through the second sun are determined based on the amount of coal passing through the first screen and the amount of coal passing through the second screen respectively. The amount of coal passing through the first screen is the amount of coal remaining in the coal washing plant when the power plant is at full load, and the amount of coal passing through the second screen is the amount of coal remaining in the coal washing plant when the power plant is at low load.

[0033] Based on the aforementioned correspondence, the screening ratio and fine coal lump size of the coal quantity on the first or second screen are adjusted, and the optimal sales plan is determined.

[0034] In some embodiments of this application, the device is further used for:

[0035] Based on the operating status and the corresponding relationship, the mining plan for each zone of the coal seam is determined, and coal mining is carried out according to the mining plan;

[0036] Based on the optimal sales plan, the blasting hole parameters or charge structure of the mining plan are adjusted to adjust the size of the coal blocks.

[0037] By applying the above technical solutions, the coal seam is divided into multiple zones based on the original geological information, and a coal seam data model is established. A coal washing plant data model is established based on the amount of over-screened coal, under-screened coal, gangue rate, and coal quality testing information of each coal type from the coal washing plant's screening workshop. The correspondence between the coal seam data model and the coal washing plant data model is determined. The amount of under-screened coal is determined based on the power plant's long-term agreement and unit operating status. Based on the correspondence, the screening ratio of the over-screened coal and the size of the clean coal are adjusted to formulate the optimal sales plan. Through the above technical solutions, the fixed coal blending quantity for the power plant can be determined according to the power plant's long-term agreement and high / low load operating status. Based on the coal seam geological information and the relationship between different coal types in the coal washing plant, combined with the customer's required coal calorific value and clean coal size, the optimal sales plan is determined. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating an intelligent optimization method for coal production and sales in open-pit coal mines, as proposed in an embodiment of the present invention, is shown.

[0040] Figure 2 This invention presents a schematic diagram of the structure of an intelligent optimization device for coal production and sales in an open-pit coal mine, as proposed in an embodiment of the present invention.

[0041] Figure 3 This invention illustrates a schematic diagram of the structure of each zone of the coal seam to be mined, as proposed in an embodiment of the invention.

[0042] Figure 4 A schematic diagram of the coal pillar structure proposed in an embodiment of the present invention is shown;

[0043] Figure 5 A schematic diagram of the process flow of a coal washing plant screening workshop according to an embodiment of the present invention is shown. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] This invention provides an intelligent optimization method for coal production and sales in open-pit coal mines. By establishing a coal seam data model to understand the coal quality in the mining area, the method predicts the proportional relationship between different coal types and gangue. Utilizing long-term power plant agreements and coal demand under high and low load conditions, the method calculates the fixed amount of under-screened coal used by the coal washing plant for power plant supply. Based on the correspondence between the coal washing plant data model and the coal seam data model, a reasonable screening ratio and fine coal lump size are formulated to determine the optimal sales plan and maximize profits. Simultaneously, the optimal sales plan guides the on-site blasting hole parameters or charge structure, thereby controlling the lump size. By effectively constructing a systematic, precise, and efficient coal quality information management system, the method effectively achieves digital control of coal sources and coal quality in coal washing plants, enabling information management of coal selection, processing, storage, transportation, and sales, improving production efficiency and transforming traditional management models.

[0046] like Figure 1 As shown, the method includes the following steps:

[0047] Step S101: Based on the original geological information, the coal seam to be mined is divided into multiple zones, and a coal seam data model is established.

[0048] In this embodiment, original geological information is obtained, and the coal seam to be mined is divided into multiple zones based on this information, such as... Figure 3 As shown, the coal seam to be mined is divided into 4 zones in this scheme. It is understood that those skilled in the art can flexibly set the number of zones. The different number of zones will not affect the protection scope of this scheme. After dividing the coal seam to be mined into 4 zones, a coal seam data model is established. The coal seam data model specifically includes the coal seam structure and coal quality information of each zone. It can be understood that the coal seam data model can reflect the coal situation in each zone.

[0049] In order to establish a coal seam data model, in some embodiments of this application, the mined coal seam is divided into multiple zones based on the original geological information, and a coal seam data model is established, specifically as follows:

[0050] Based on the original geological information, additional exploration boreholes were drilled in the mined coal seam, and the mined coal seam was divided into multiple zones;

[0051] Determine the coal pillar structure within each zone, wherein the coal pillar structure includes the calorific value of the coal, the hardness of the coal, and the gangue content;

[0052] Based on the coal pillar structure, the coal seam structure and coal quality information of each zone are determined, and a coal seam data model is established.

[0053] In this embodiment, supplementary exploration boreholes are drilled into the coal seam using original geological information and supplementary borehole data, while simultaneously... Figure 3 As shown, the coal seam is divided into four zones, and the coal pillar structure of each zone is determined. A schematic diagram of the coal pillar structure is shown below. Figure 4 As shown, this includes coal calorific value, coal hardness, and gangue content. After determining the coal pillar structure, the coal seam structure and coal quality information in each zone can be further determined, thus obtaining a coal seam data model. Through the coal seam data model, the coal quality of the mining area can be understood, thereby predicting the proportional relationship between different coal types and gangue.

[0054] Step S102: Based on the amount of coal on the screen, the amount of coal under the screen, the gangue rate, and the coal quality test information of each type of coal in the screening workshop of the coal washing plant, establish a data model of the coal washing plant, and determine the correspondence between the coal seam data model and the coal washing plant data model.

[0055] In this embodiment, coal washing is an indispensable step in the deep processing of coal. Coal directly mined from the mine is called raw coal. During the mining process, raw coal is mixed with many impurities, and the quality of coal also varies, with coals with low and high ash content mixed together. Coal washing is an industrial process that removes impurities from raw coal or separates high-quality coal from low-quality coal.

[0056] Coal extracted from the coal seam is sent to a coal washing plant for washing operations to separate the oversize and undersize coal, such as... Figure 5 As shown, in Figure 5 Sampling was conducted in the area shown to determine the gangue rate and coal quality test information for each coal type at different sampling points. Simultaneously, sampling was conducted at the under-screen and over-screen coal locations to obtain the amount of over-screen coal, under-screen coal, gangue rate, and coal quality test information for each coal type. Based on the sampling results from the above sampling points, a coal washing plant data model can be established, and the correspondence between the coal seam data model and the coal washing plant data model can be determined.

[0057] Step S103: Determine the amount of under-screened coal based on the power plant's long-term agreement and the unit's operating status, and adjust the screening ratio and fine coal lump size of the over-screened coal based on the corresponding relationship to formulate the optimal sales plan.

[0058] In this embodiment, the fixed coal allocation for the power plant can be determined through the long-term agreement of the power plant and the operating status of the unit. That is, the amount of under-screened coal is set to a fixed value, and the amount of remaining over-screened coal can also be determined. Since the coal washing plant data model has been determined in the above steps, the coal information of the over-screened coal can be known. The screening ratio and fine coal lump size of the over-screened coal are adjusted through the correspondence between the coal seam data model and the coal washing plant data model to determine the optimal sales plan.

[0059] In order to determine the amount of coal screened out, in some embodiments of this application, the unit operating state includes full load state and low load state.

[0060] To determine the amount of undersized coal and adjust the screening ratio and clean coal lump size of the oversized coal, in some embodiments of this application, the amount of undersized coal is determined based on the power plant's long-term agreement and unit operating status, and the screening ratio and clean coal lump size of the oversized coal are adjusted based on the corresponding relationship, specifically as follows:

[0061] Determine the amount of coal passing through the first screen when the power plant is at full load and the amount of coal passing through the second screen when the power plant is at low load.

[0062] The amount of coal passing through the first screen and the amount of coal passing through the second sun are determined based on the amount of coal passing through the first screen and the amount of coal passing through the second screen respectively. The amount of coal passing through the first screen is the amount of coal remaining in the coal washing plant when the power plant is at full load, and the amount of coal passing through the second screen is the amount of coal remaining in the coal washing plant when the power plant is at low load.

[0063] Based on the aforementioned correspondence, the screening ratio and fine coal lump size of the coal quantity on the first or second screen are adjusted, and the optimal sales plan is determined.

[0064] In this embodiment, the fixed coal consumption of the power plant under full load is set as the first under-screen coal quantity, and the fixed coal consumption of the power plant under low load is set as the second under-sun coal quantity. After obtaining the first under-sun coal quantity and the second under-screen coal quantity, the remaining first-screen coal quantity of the coal washing plant under full load and the remaining second-screen coal quantity of the coal washing plant under low load can be further obtained. After obtaining the first-screen coal quantity and the second-screen coal quantity, the screening ratio and fine coal lump size of the first-screen coal quantity or the second-screen coal quantity are adjusted according to the correspondence between the coal seam data model and the coal washing plant data model to determine the optimal sales plan. At the same time, when determining the optimal sales plan, it is also necessary to consider the current mining cost and sales price, sign sales contracts with customers for different lump sizes and calorific values, and formulate a reasonable sales plan to maximize profits.

[0065] To further enable intelligent management of production organization, refined coal sales, and power plant coal consumption in integrated coal-fired power projects, in some embodiments of this application, the method further includes:

[0066] Based on the operating status and the corresponding relationship, the mining plan for each zone of the coal seam is determined, and coal mining is carried out according to the mining plan;

[0067] Based on the optimal sales plan, the blasting hole parameters or charge structure of the mining plan are adjusted to adjust the size of the coal blocks.

[0068] In this embodiment, the mining plan for the coal seam is adjusted based on the power plant's operating status and the correspondence between the coal seam data model and the coal washing plant data model, in order to meet the needs of the power plant and sales. At the same time, the blasting hole network parameters or charge structure of the mining plan need to be adjusted according to the customer's needs or sales plan to meet the customer's or the final sales plan's requirements for coal block size.

[0069] By applying the above technical solutions, the coal seam is divided into multiple zones based on the original geological information, and a coal seam data model is established. A coal washing plant data model is established based on the amount of over-screened coal, under-screened coal, gangue rate, and coal quality testing information of each coal type in the coal washing plant's screening workshop. The correspondence between the coal seam data model and the coal washing plant data model is determined. The amount of under-screened coal is determined based on the power plant's long-term agreement and unit operating status. Based on the correspondence, the screening ratio of the over-screened coal and the size of the clean coal are adjusted, and an optimal sales plan is formulated. Through the above technical solutions, the fixed coal blending quantity for the power plant can be determined according to the power plant's long-term agreement and high / low load operating status. Based on the coal seam geological information and the relationship between different coal types in the coal washing plant, combined with the customer's required coal calorific value and clean coal size, an optimal sales plan is determined.

[0070] In summary, this solution establishes a coal seam data model to understand the coal quality in the mining area, thereby predicting the proportional relationship between different coal types and gangue. Utilizing long-term power plant agreements and coal demand under high and low load conditions, the fixed amount of under-screened coal used by the coal washing plant for power plant supply is calculated. Based on the correspondence between the coal washing plant data model and the coal seam data model, a reasonable screening ratio and fine coal lump size are determined, identifying the optimal sales plan to maximize profits. Simultaneously, the optimal sales plan guides the on-site blasting hole parameters or charge structure, thereby controlling lump size. By effectively constructing a systematic, precise, and efficient coal quality information management system, the coal source and quality of the coal washing plant can be effectively digitally controlled, enabling information management of coal selection, processing, storage, transportation, and sales, improving production efficiency and transforming traditional management models.

[0071] This application also proposes an intelligent optimization device for coal production and sales in open-pit coal mines, such as... Figure 2 As shown, the device includes:

[0072] Module 10 is established to divide the coal seam into multiple zones based on the original geological information and to establish a coal seam data model.

[0073] The determination module 20 is used to establish a coal washing plant data model based on the amount of coal on the screen, the amount of coal under the screen, the gangue rate, and the coal quality test information of each type of coal in the coal washing plant screening workshop, and to determine the correspondence between the coal seam data model and the coal washing plant data model.

[0074] The formulation module 30 is used to determine the amount of under-screened coal based on the power plant's long-term agreement and the unit's operating status, and to adjust the screening ratio and fine coal lump size of the over-screened coal based on the corresponding relationship, so as to formulate the optimal sales plan.

[0075] In specific application scenarios, the establishment module is specifically used for:

[0076] Based on the original geological information, additional exploration boreholes were drilled in the mined coal seam, and the mined coal seam was divided into multiple zones;

[0077] Determine the coal pillar structure within each zone, wherein the coal pillar structure includes the calorific value of the coal, the hardness of the coal, and the gangue content;

[0078] Based on the coal pillar structure, the coal seam structure and coal quality information of each zone are determined, and a coal seam data model is established.

[0079] In specific application scenarios, the unit's operating status includes full load status and low load status.

[0080] In specific application scenarios, the designation module is specifically used for:

[0081] Determine the amount of coal passing through the first screen when the power plant is at full load and the amount of coal passing through the second screen when the power plant is at low load.

[0082] The amount of coal passing through the first screen and the amount of coal passing through the second sun are determined based on the amount of coal passing through the first screen and the amount of coal passing through the second screen respectively. The amount of coal passing through the first screen is the amount of coal remaining in the coal washing plant when the power plant is at full load, and the amount of coal passing through the second screen is the amount of coal remaining in the coal washing plant when the power plant is at low load.

[0083] Based on the aforementioned correspondence, the screening ratio and fine coal lump size of the coal quantity on the first or second screen are adjusted, and the optimal sales plan is determined.

[0084] In specific application scenarios, the device is also used for:

[0085] Based on the operating status and the corresponding relationship, the mining plan for each zone of the coal seam is determined, and coal mining is carried out according to the mining plan;

[0086] Based on the optimal sales plan, the blasting hole parameters or charge structure of the mining plan are adjusted to adjust the size of the coal blocks.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent optimization of coal production and sales in open-pit coal mines, characterized in that, The method includes: Based on the original geological information, the coal seam to be mined is divided into multiple zones, and a coal seam data model is established. A coal washing plant data model is established based on the amount of coal on the screen, the amount of coal under the screen, the gangue rate, and the coal quality test information of each type of coal in the coal washing plant screening workshop, and the correspondence between the coal seam data model and the coal washing plant data model is determined. The amount of under-screened coal is determined based on the power plant's long-term agreement and the unit's operating status. Based on the corresponding relationship, the screening ratio and fine coal lump size of the over-screened coal are adjusted to formulate the optimal sales plan. Based on the original geological information, the coal seam to be mined is divided into multiple zones, and a coal seam data model is established, specifically as follows: Based on the original geological information, additional exploration boreholes were drilled in the mined coal seam, and the mined coal seam was divided into multiple zones; Determine the coal pillar structure within each zone, wherein the coal pillar structure includes the calorific value of the coal, the hardness of the coal, and the gangue content; Based on the coal pillar structure, the coal seam structure and coal quality information of each zone are determined, and a coal seam data model is established. The unit's operating status includes full load status and low load status; The amount of under-screened coal is determined based on the power plant's long-term agreement and unit operating status, and the screening ratio and fine coal lump size are adjusted based on the corresponding relationship, specifically as follows: Determine the amount of coal passing through the first screen when the power plant is at full load and the amount of coal passing through the second screen when the power plant is at low load. The amount of coal passing through the first screen and the amount of coal passing through the second sun are determined based on the amount of coal passing through the first screen and the amount of coal passing through the second screen respectively. The amount of coal passing through the first screen is the amount of coal remaining in the coal washing plant when the power plant is at full load, and the amount of coal passing through the second screen is the amount of coal remaining in the coal washing plant when the power plant is at low load. Based on the aforementioned correspondence, the screening ratio and fine coal lump size of the coal quantity on the first or second screen are adjusted, and the optimal sales plan is determined. The method further includes: Based on the operating status and the corresponding relationship, the mining plan for each zone of the coal seam is determined, and coal mining is carried out according to the mining plan; Based on the optimal sales plan, the blasting hole parameters or charge structure of the mining plan are adjusted to adjust the size of the coal blocks.

2. An intelligent optimization device for coal production and sales in open-pit coal mines, used to implement the intelligent optimization method for coal production and sales in open-pit coal mines as described in claim 1, characterized in that, The device includes: A module is established to divide the coal seam into multiple zones based on the original geological information and to build a coal seam data model. The determination module is used to establish a coal washing plant data model based on the amount of coal on the screen, the amount of coal under the screen, the gangue rate, and the coal quality test information of each type of coal in the coal washing plant screening workshop, and to determine the correspondence between the coal seam data model and the coal washing plant data model. The module is used to determine the amount of under-screened coal based on the power plant's long-term agreement and the unit's operating status, and to adjust the screening ratio and fine coal lump size of the over-screened coal based on the corresponding relationship, thereby formulating the optimal sales plan.

3. The device as described in claim 2, characterized in that, The establishment module is specifically used for: Based on the original geological information, additional exploration boreholes were drilled in the mined coal seam, and the mined coal seam was divided into multiple zones; Determine the coal pillar structure within each zone, wherein the coal pillar structure includes the calorific value of the coal, the hardness of the coal, and the gangue content; Based on the coal pillar structure, the coal seam structure and coal quality information of each zone are determined, and a coal seam data model is established.

4. The device as described in claim 2, characterized in that, The unit's operating status includes full load status and low load status.

5. The device as described in claim 4, characterized in that, The formulation module is specifically used for: Determine the amount of coal passing through the first screen when the power plant is at full load and the amount of coal passing through the second screen when the power plant is at low load. The amount of coal passing through the first screen and the amount of coal passing through the second sun are determined based on the amount of coal passing through the first screen and the amount of coal passing through the second screen respectively. The amount of coal passing through the first screen is the amount of coal remaining in the coal washing plant when the power plant is at full load, and the amount of coal passing through the second screen is the amount of coal remaining in the coal washing plant when the power plant is at low load. Based on the aforementioned correspondence, the screening ratio and fine coal lump size of the coal quantity on the first or second screen are adjusted, and the optimal sales plan is determined.

6. The device as described in claim 5, characterized in that, The device is also used for: Based on the operating status and the corresponding relationship, the mining plan for each zone of the coal seam is determined, and coal mining is carried out according to the mining plan; Based on the optimal sales plan, the blasting hole parameters or charge structure of the mining plan are adjusted to adjust the size of the coal blocks.

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